Overview

Dataset statistics

Number of variables19
Number of observations456
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.2 KiB
Average record size in memory144.3 B

Variable types

Numeric14
Categorical5

Alerts

FTHG is highly correlated with HTHG and 4 other fieldsHigh correlation
FTAG is highly correlated with HTAGHigh correlation
HTHG is highly correlated with FTHGHigh correlation
HTAG is highly correlated with FTAGHigh correlation
HS is highly correlated with FTHG and 5 other fieldsHigh correlation
AS is highly correlated with HS and 5 other fieldsHigh correlation
HST is highly correlated with FTHG and 1 other fieldsHigh correlation
AST is highly correlated with AS and 2 other fieldsHigh correlation
HC is highly correlated with HS and 3 other fieldsHigh correlation
AC is highly correlated with ASHigh correlation
Home is highly correlated with FTHG and 5 other fieldsHigh correlation
Away is highly correlated with FTHG and 5 other fieldsHigh correlation
FTHG is highly correlated with HTHG and 1 other fieldsHigh correlation
FTAG is highly correlated with HTAGHigh correlation
HTHG is highly correlated with FTHGHigh correlation
HTAG is highly correlated with FTAGHigh correlation
HS is highly correlated with AS and 4 other fieldsHigh correlation
AS is highly correlated with HS and 4 other fieldsHigh correlation
HST is highly correlated with FTHG and 1 other fieldsHigh correlation
AST is highly correlated with AS and 2 other fieldsHigh correlation
HC is highly correlated with HS and 2 other fieldsHigh correlation
AC is highly correlated with ASHigh correlation
Home is highly correlated with HS and 4 other fieldsHigh correlation
Away is highly correlated with HS and 4 other fieldsHigh correlation
FTHG is highly correlated with HTHG and 1 other fieldsHigh correlation
FTAG is highly correlated with HTAGHigh correlation
HTHG is highly correlated with FTHGHigh correlation
HTAG is highly correlated with FTAGHigh correlation
HS is highly correlated with HST and 2 other fieldsHigh correlation
AS is highly correlated with AST and 2 other fieldsHigh correlation
HST is highly correlated with FTHG and 1 other fieldsHigh correlation
AST is highly correlated with ASHigh correlation
Home is highly correlated with HS and 2 other fieldsHigh correlation
Away is highly correlated with HS and 2 other fieldsHigh correlation
Away is highly correlated with HomeHigh correlation
Home is highly correlated with AwayHigh correlation
FTHG is highly correlated with HTHG and 1 other fieldsHigh correlation
FTAG is highly correlated with HTAG and 1 other fieldsHigh correlation
HTHG is highly correlated with FTHGHigh correlation
HTAG is highly correlated with FTAG and 1 other fieldsHigh correlation
HS is highly correlated with AS and 4 other fieldsHigh correlation
AS is highly correlated with HS and 4 other fieldsHigh correlation
HST is highly correlated with FTHG and 2 other fieldsHigh correlation
AST is highly correlated with FTAG and 4 other fieldsHigh correlation
HC is highly correlated with HS and 3 other fieldsHigh correlation
AC is highly correlated with AS and 2 other fieldsHigh correlation
Home is highly correlated with HS and 5 other fieldsHigh correlation
Away is highly correlated with HS and 5 other fieldsHigh correlation
Home is uniformly distributed Uniform
Away is uniformly distributed Uniform
FTHG has 107 (23.5%) zeros Zeros
FTAG has 148 (32.5%) zeros Zeros
HTHG has 214 (46.9%) zeros Zeros
HTAG has 256 (56.1%) zeros Zeros
HST has 13 (2.9%) zeros Zeros
AST has 18 (3.9%) zeros Zeros
HC has 10 (2.2%) zeros Zeros
AC has 13 (2.9%) zeros Zeros
HY has 105 (23.0%) zeros Zeros
AY has 63 (13.8%) zeros Zeros

Reproduction

Analysis started2022-08-11 16:09:39.238081
Analysis finished2022-08-11 16:11:19.537416
Duration1 minute and 40.3 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

FTHG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.782894737
Minimum0
Maximum8
Zeros107
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:19.860532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.548843297
Coefficient of variation (CV)0.8687239156
Kurtosis0.5761832703
Mean1.782894737
Median Absolute Deviation (MAD)1
Skewness0.8903394288
Sum813
Variance2.398915558
MonotonicityNot monotonic
2022-08-11T21:41:20.022012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1118
25.9%
0107
23.5%
2103
22.6%
366
14.5%
433
 
7.2%
519
 
4.2%
67
 
1.5%
72
 
0.4%
81
 
0.2%
ValueCountFrequency (%)
0107
23.5%
1118
25.9%
2103
22.6%
366
14.5%
433
 
7.2%
519
 
4.2%
67
 
1.5%
72
 
0.4%
81
 
0.2%
ValueCountFrequency (%)
81
 
0.2%
72
 
0.4%
67
 
1.5%
519
 
4.2%
433
 
7.2%
366
14.5%
2103
22.6%
1118
25.9%
0107
23.5%

FTAG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.346491228
Minimum0
Maximum6
Zeros148
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:20.196104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.345120923
Coefficient of variation (CV)0.9989823145
Kurtosis0.8738614524
Mean1.346491228
Median Absolute Deviation (MAD)1
Skewness1.069622398
Sum614
Variance1.809350299
MonotonicityNot monotonic
2022-08-11T21:41:20.349012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0148
32.5%
1138
30.3%
287
19.1%
348
 
10.5%
422
 
4.8%
58
 
1.8%
65
 
1.1%
ValueCountFrequency (%)
0148
32.5%
1138
30.3%
287
19.1%
348
 
10.5%
422
 
4.8%
58
 
1.8%
65
 
1.1%
ValueCountFrequency (%)
65
 
1.1%
58
 
1.8%
422
 
4.8%
348
 
10.5%
287
19.1%
1138
30.3%
0148
32.5%

HTHG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7785087719
Minimum0
Maximum5
Zeros214
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:20.515442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9073925765
Coefficient of variation (CV)1.165552155
Kurtosis1.518867628
Mean0.7785087719
Median Absolute Deviation (MAD)1
Skewness1.214041547
Sum355
Variance0.8233612878
MonotonicityNot monotonic
2022-08-11T21:41:20.681340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0214
46.9%
1157
34.4%
263
 
13.8%
317
 
3.7%
44
 
0.9%
51
 
0.2%
ValueCountFrequency (%)
0214
46.9%
1157
34.4%
263
 
13.8%
317
 
3.7%
44
 
0.9%
51
 
0.2%
ValueCountFrequency (%)
51
 
0.2%
44
 
0.9%
317
 
3.7%
263
 
13.8%
1157
34.4%
0214
46.9%

HTAG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6228070175
Minimum0
Maximum5
Zeros256
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:20.843241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8530782104
Coefficient of variation (CV)1.369731211
Kurtosis2.764802413
Mean0.6228070175
Median Absolute Deviation (MAD)0
Skewness1.554441075
Sum284
Variance0.727742433
MonotonicityNot monotonic
2022-08-11T21:41:21.000142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0256
56.1%
1138
30.3%
245
 
9.9%
313
 
2.9%
43
 
0.7%
51
 
0.2%
ValueCountFrequency (%)
0256
56.1%
1138
30.3%
245
 
9.9%
313
 
2.9%
43
 
0.7%
51
 
0.2%
ValueCountFrequency (%)
51
 
0.2%
43
 
0.7%
313
 
2.9%
245
 
9.9%
1138
30.3%
0256
56.1%

HS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.70394737
Minimum0
Maximum43
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:21.367896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile25
Maximum43
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.488372168
Coefficient of variation (CV)0.4734673882
Kurtosis0.4969228272
Mean13.70394737
Median Absolute Deviation (MAD)4
Skewness0.593353819
Sum6249
Variance42.0989734
MonotonicityNot monotonic
2022-08-11T21:41:21.575766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1033
 
7.2%
1229
 
6.4%
1628
 
6.1%
1128
 
6.1%
1327
 
5.9%
1526
 
5.7%
1925
 
5.5%
925
 
5.5%
823
 
5.0%
2020
 
4.4%
Other values (24)192
42.1%
ValueCountFrequency (%)
01
 
0.2%
25
 
1.1%
38
 
1.8%
413
 
2.9%
515
3.3%
617
3.7%
720
4.4%
823
5.0%
925
5.5%
1033
7.2%
ValueCountFrequency (%)
431
 
0.2%
351
 
0.2%
322
 
0.4%
311
 
0.2%
301
 
0.2%
291
 
0.2%
285
1.1%
273
0.7%
267
1.5%
256
1.3%

AS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.45614035
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:21.770028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median11
Q315
95-th percentile22
Maximum28
Range27
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.782718249
Coefficient of variation (CV)0.5047701994
Kurtosis-0.4552146649
Mean11.45614035
Median Absolute Deviation (MAD)4
Skewness0.5211942598
Sum5224
Variance33.43983035
MonotonicityNot monotonic
2022-08-11T21:41:21.971903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
938
 
8.3%
733
 
7.2%
533
 
7.2%
631
 
6.8%
1129
 
6.4%
827
 
5.9%
1225
 
5.5%
1022
 
4.8%
1522
 
4.8%
1322
 
4.8%
Other values (18)174
38.2%
ValueCountFrequency (%)
11
 
0.2%
28
 
1.8%
315
 
3.3%
416
3.5%
533
7.2%
631
6.8%
733
7.2%
827
5.9%
938
8.3%
1022
4.8%
ValueCountFrequency (%)
281
 
0.2%
272
 
0.4%
263
 
0.7%
253
 
0.7%
245
1.1%
234
 
0.9%
229
2.0%
2111
2.4%
2011
2.4%
1911
2.4%

HST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.85745614
Minimum0
Maximum24
Zeros13
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:22.163026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile12
Maximum24
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.570247829
Coefficient of variation (CV)0.6095219056
Kurtosis1.390920986
Mean5.85745614
Median Absolute Deviation (MAD)2
Skewness0.8453990886
Sum2671
Variance12.74666956
MonotonicityNot monotonic
2022-08-11T21:41:22.342899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
457
12.5%
553
11.6%
348
10.5%
646
10.1%
740
8.8%
235
7.7%
832
7.0%
130
6.6%
930
6.6%
1023
 
5.0%
Other values (10)62
13.6%
ValueCountFrequency (%)
013
 
2.9%
130
6.6%
235
7.7%
348
10.5%
457
12.5%
553
11.6%
646
10.1%
740
8.8%
832
7.0%
930
6.6%
ValueCountFrequency (%)
241
 
0.2%
191
 
0.2%
181
 
0.2%
164
 
0.9%
152
 
0.4%
142
 
0.4%
132
 
0.4%
1217
3.7%
1119
4.2%
1023
5.0%

AST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.833333333
Minimum0
Maximum20
Zeros18
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:22.526179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q37
95-th percentile11
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.069643403
Coefficient of variation (CV)0.6350986351
Kurtosis1.338507461
Mean4.833333333
Median Absolute Deviation (MAD)2
Skewness0.9034497361
Sum2204
Variance9.422710623
MonotonicityNot monotonic
2022-08-11T21:41:22.703755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
261
13.4%
461
13.4%
361
13.4%
553
11.6%
648
10.5%
740
8.8%
134
7.5%
825
5.5%
920
 
4.4%
018
 
3.9%
Other values (7)35
7.7%
ValueCountFrequency (%)
018
 
3.9%
134
7.5%
261
13.4%
361
13.4%
461
13.4%
553
11.6%
648
10.5%
740
8.8%
825
5.5%
920
 
4.4%
ValueCountFrequency (%)
201
 
0.2%
171
 
0.2%
142
 
0.4%
132
 
0.4%
129
 
2.0%
119
 
2.0%
1011
 
2.4%
920
4.4%
825
5.5%
740
8.8%

HF
Real number (ℝ≥0)

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.13596491
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:22.896638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median10
Q312
95-th percentile16
Maximum21
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.446791843
Coefficient of variation (CV)0.3400556211
Kurtosis-0.07635334074
Mean10.13596491
Median Absolute Deviation (MAD)2
Skewness0.2958438778
Sum4622
Variance11.88037401
MonotonicityNot monotonic
2022-08-11T21:41:23.073418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1153
11.6%
953
11.6%
1247
10.3%
1046
10.1%
843
9.4%
1338
8.3%
633
7.2%
730
6.6%
528
6.1%
1422
 
4.8%
Other values (10)63
13.8%
ValueCountFrequency (%)
22
 
0.4%
32
 
0.4%
411
 
2.4%
528
6.1%
633
7.2%
730
6.6%
843
9.4%
953
11.6%
1046
10.1%
1153
11.6%
ValueCountFrequency (%)
212
 
0.4%
201
 
0.2%
194
 
0.9%
183
 
0.7%
179
 
2.0%
1610
 
2.2%
1519
4.2%
1422
4.8%
1338
8.3%
1247
10.3%

AF
Real number (ℝ≥0)

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.04824561
Minimum2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:23.276555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median10
Q312
95-th percentile16
Maximum24
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.446590478
Coefficient of variation (CV)0.343004203
Kurtosis0.2139603135
Mean10.04824561
Median Absolute Deviation (MAD)2
Skewness0.3970287689
Sum4582
Variance11.87898593
MonotonicityNot monotonic
2022-08-11T21:41:23.449445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1060
13.2%
954
11.8%
749
10.7%
845
9.9%
1137
8.1%
1335
7.7%
1234
7.5%
1430
6.6%
628
6.1%
1524
 
5.3%
Other values (10)60
13.2%
ValueCountFrequency (%)
21
 
0.2%
38
 
1.8%
49
 
2.0%
518
 
3.9%
628
6.1%
749
10.7%
845
9.9%
954
11.8%
1060
13.2%
1137
8.1%
ValueCountFrequency (%)
241
 
0.2%
203
 
0.7%
192
 
0.4%
185
 
1.1%
173
 
0.7%
1610
 
2.2%
1524
5.3%
1430
6.6%
1335
7.7%
1234
7.5%

HC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.962719298
Minimum0
Maximum19
Zeros10
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:23.635616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile12
Maximum19
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.49642399
Coefficient of variation (CV)0.5863807796
Kurtosis0.5344533678
Mean5.962719298
Median Absolute Deviation (MAD)2
Skewness0.7464268258
Sum2719
Variance12.22498072
MonotonicityNot monotonic
2022-08-11T21:41:23.809508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
356
12.3%
455
12.1%
554
11.8%
847
10.3%
643
9.4%
739
8.6%
233
7.2%
925
5.5%
124
5.3%
1022
 
4.8%
Other values (10)58
12.7%
ValueCountFrequency (%)
010
 
2.2%
124
5.3%
233
7.2%
356
12.3%
455
12.1%
554
11.8%
643
9.4%
739
8.6%
847
10.3%
925
5.5%
ValueCountFrequency (%)
191
 
0.2%
181
 
0.2%
172
 
0.4%
164
 
0.9%
151
 
0.2%
144
 
0.9%
139
2.0%
1210
2.2%
1116
3.5%
1022
4.8%

AC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.903508772
Minimum0
Maximum16
Zeros13
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:23.999279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q37
95-th percentile11
Maximum16
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.060472701
Coefficient of variation (CV)0.6241393345
Kurtosis0.3265221555
Mean4.903508772
Median Absolute Deviation (MAD)2
Skewness0.7260309095
Sum2236
Variance9.366493156
MonotonicityNot monotonic
2022-08-11T21:41:24.175172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
470
15.4%
354
11.8%
652
11.4%
252
11.4%
146
10.1%
542
9.2%
741
9.0%
834
7.5%
916
 
3.5%
013
 
2.9%
Other values (6)36
7.9%
ValueCountFrequency (%)
013
 
2.9%
146
10.1%
252
11.4%
354
11.8%
470
15.4%
542
9.2%
652
11.4%
741
9.0%
834
7.5%
916
 
3.5%
ValueCountFrequency (%)
161
 
0.2%
143
 
0.7%
139
 
2.0%
122
 
0.4%
1110
 
2.2%
1011
 
2.4%
916
 
3.5%
834
7.5%
741
9.0%
652
11.4%

HY
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.489035088
Minimum0
Maximum6
Zeros105
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:24.358982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.198850343
Coefficient of variation (CV)0.805118934
Kurtosis0.1203392228
Mean1.489035088
Median Absolute Deviation (MAD)1
Skewness0.6639754279
Sum679
Variance1.437242144
MonotonicityNot monotonic
2022-08-11T21:41:24.512885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1144
31.6%
2122
26.8%
0105
23.0%
356
 
12.3%
423
 
5.0%
55
 
1.1%
61
 
0.2%
ValueCountFrequency (%)
0105
23.0%
1144
31.6%
2122
26.8%
356
 
12.3%
423
 
5.0%
55
 
1.1%
61
 
0.2%
ValueCountFrequency (%)
61
 
0.2%
55
 
1.1%
423
 
5.0%
356
 
12.3%
2122
26.8%
1144
31.6%
0105
23.0%

AY
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.811403509
Minimum0
Maximum6
Zeros63
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2022-08-11T21:41:24.676260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.227684949
Coefficient of variation (CV)0.6777534342
Kurtosis0.06641996586
Mean1.811403509
Median Absolute Deviation (MAD)1
Skewness0.514257905
Sum826
Variance1.507210334
MonotonicityNot monotonic
2022-08-11T21:41:24.825168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1139
30.5%
2126
27.6%
389
19.5%
063
13.8%
430
 
6.6%
56
 
1.3%
63
 
0.7%
ValueCountFrequency (%)
063
13.8%
1139
30.5%
2126
27.6%
389
19.5%
430
 
6.6%
56
 
1.3%
63
 
0.7%
ValueCountFrequency (%)
63
 
0.7%
56
 
1.3%
430
 
6.6%
389
19.5%
2126
27.6%
1139
30.5%
063
13.8%

HR
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0.0
430 
1.0
 
24
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1368
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0430
94.3%
1.024
 
5.3%
2.02
 
0.4%

Length

2022-08-11T21:41:25.001834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T21:41:25.235605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0430
94.3%
1.024
 
5.3%
2.02
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0886
64.8%
.456
33.3%
124
 
1.8%
22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number912
66.7%
Other Punctuation456
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0886
97.1%
124
 
2.6%
22
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0886
64.8%
.456
33.3%
124
 
1.8%
22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0886
64.8%
.456
33.3%
124
 
1.8%
22
 
0.1%

AR
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0.0
421 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1368
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0421
92.3%
1.035
 
7.7%

Length

2022-08-11T21:41:25.384568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T21:41:25.714365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0421
92.3%
1.035
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0877
64.1%
.456
33.3%
135
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number912
66.7%
Other Punctuation456
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0877
96.2%
135
 
3.8%
Other Punctuation
ValueCountFrequency (%)
.456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0877
64.1%
.456
33.3%
135
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0877
64.1%
.456
33.3%
135
 
2.6%

Label
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1.0
300 
0.0
79 
-1.0
77 

Length

Max length4
Median length3
Mean length3.168859649
Min length3

Characters and Unicode

Total characters1445
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0300
65.8%
0.079
 
17.3%
-1.077
 
16.9%

Length

2022-08-11T21:41:25.875550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T21:41:26.060914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0377
82.7%
0.079
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0535
37.0%
.456
31.6%
1377
26.1%
-77
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number912
63.1%
Other Punctuation456
31.6%
Dash Punctuation77
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0535
58.7%
1377
41.3%
Other Punctuation
ValueCountFrequency (%)
.456
100.0%
Dash Punctuation
ValueCountFrequency (%)
-77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1445
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0535
37.0%
.456
31.6%
1377
26.1%
-77
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0535
37.0%
.456
31.6%
1377
26.1%
-77
 
5.3%

Home
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
228 
0
228 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters456
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1228
50.0%
0228
50.0%

Length

2022-08-11T21:41:26.220527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T21:41:26.388279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1228
50.0%
0228
50.0%

Most occurring characters

ValueCountFrequency (%)
1228
50.0%
0228
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number456
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1228
50.0%
0228
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1228
50.0%
0228
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1228
50.0%
0228
50.0%

Away
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
0
228 
1
228 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters456
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0228
50.0%
1228
50.0%

Length

2022-08-11T21:41:26.529727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-11T21:41:26.698888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0228
50.0%
1228
50.0%

Most occurring characters

ValueCountFrequency (%)
0228
50.0%
1228
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number456
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0228
50.0%
1228
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0228
50.0%
1228
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0228
50.0%
1228
50.0%

Interactions

2022-08-11T21:41:15.161400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:38.260391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:41.572144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:44.366499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:46.912660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:49.649243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:52.186377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:54.944322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:57.686624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:00.725292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:03.534706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:06.638782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:09.570966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:12.542351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:15.517748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:38.991737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:41.769861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:44.556380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:47.109539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:49.838128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:52.379258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:55.140198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:57.917480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:00.926170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:03.718589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:06.848654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:09.787548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:12.742227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:15.701633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:39.186614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:41.957744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:44.737268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:47.293424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:50.025010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:52.561146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:55.323086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:58.160332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:01.122046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:03.897479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:07.047529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:09.986198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:12.947097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:15.880522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:39.371587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:42.140875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:44.912162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:47.470315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:50.198495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:52.737038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:55.501976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:58.352210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:01.308932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:04.070372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:07.237411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:10.177080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:13.127986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:16.069406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:39.564752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:42.321764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:45.090050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:47.641209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:50.381384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:52.910929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:55.689857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:58.551090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:01.537856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:04.299232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:07.446282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:10.571836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:13.302878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:16.247298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:39.748640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:42.492659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:45.257948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:47.807105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:50.550154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:53.081822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:55.854757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:58.734973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:01.749724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:04.488112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:07.689131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:10.748726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:13.477561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:16.436178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:39.936522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:42.680543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:45.436838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:47.983996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:50.722045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:53.272707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:56.030647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:58.927854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:01.942604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:04.898858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:07.875016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:10.938610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:13.654459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:16.617066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:40.127404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:42.859433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:45.611727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:48.157889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:50.899939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:53.445597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:56.207538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:59.125398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:02.128489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:05.101736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:08.076891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:11.124495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:13.831222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:16.819941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:40.338273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:43.066303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:45.810606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:48.354766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:51.096815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:53.644475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:56.408415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:59.502050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:02.343924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:05.320598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:08.299753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:11.327369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:14.032099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:17.025816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:40.596747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:43.265180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:46.003485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:48.544977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:51.290932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:53.835356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:56.596296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:59.702925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:02.550703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:05.517475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:08.513621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:11.530416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:14.226977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:17.204702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:40.776635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:43.439074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:46.171381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:48.714872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:51.456829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:54.166152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:56.769190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:59.912796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:02.732306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:05.744339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:08.757473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:11.736288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:14.400870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:17.399585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:40.975515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:43.635952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:46.360264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:49.078597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:51.646711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:54.373024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:57.008044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:00.130660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:02.942069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:06.003175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:08.962343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:11.952716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:14.596750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:17.594470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:41.181385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:44.001725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:46.551147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:49.280471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:51.832596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:54.568902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:57.229907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:00.337533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:03.147943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:06.265014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:09.168216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:12.153589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:14.788630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:17.816337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:41.379262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:44.181611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:46.732033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:49.462358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:52.013485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:54.755438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:40:57.462761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:00.532414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:03.338824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:06.447901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:09.368093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:12.351466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-11T21:41:14.977513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-11T21:41:26.907707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-11T21:41:27.304396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-11T21:41:27.664172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-11T21:41:27.991968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-11T21:41:28.223827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-11T21:41:18.186157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-11T21:41:18.847288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FTHGFTAGHTHGHTAGHSASHSTASTHFAFHCACHYAYHRARLabelHomeAway
01.00.01.00.016.010.011.02.07.011.05.06.00.01.00.00.01.010
14.02.01.00.010.019.06.09.013.012.03.012.03.02.00.00.01.010
23.01.02.01.020.012.012.08.018.014.011.05.01.01.00.00.01.010
32.02.00.00.020.011.07.05.011.012.014.02.00.02.00.00.00.010
43.03.01.02.013.06.08.04.010.011.08.03.01.03.00.00.00.010
51.01.01.00.017.08.07.03.014.012.08.02.01.03.00.00.00.010
62.01.01.01.013.010.08.06.012.017.09.06.01.06.00.00.01.010
74.03.03.02.011.07.05.05.013.08.06.04.02.02.00.01.01.010
82.00.02.00.012.07.06.03.013.010.07.06.00.02.00.00.01.010
94.01.02.00.012.08.06.03.06.010.04.03.01.01.00.00.01.010

Last rows

FTHGFTAGHTHGHTAGHSASHSTASTHFAFHCACHYAYHRARLabelHomeAway
4460.02.00.02.02.017.00.06.08.05.01.03.02.01.00.00.01.001
4471.04.00.00.08.08.03.05.013.08.06.01.02.01.00.00.01.001
4481.03.01.01.03.016.02.07.08.08.00.010.02.01.00.00.01.001
4490.01.00.01.07.015.01.03.011.09.01.03.02.02.00.00.01.001
4500.03.00.00.03.08.00.07.014.013.04.02.00.00.00.00.01.001
4510.02.00.00.05.011.02.04.013.012.01.01.02.03.00.00.01.001
4521.02.01.02.08.013.03.03.012.07.01.011.00.01.01.01.01.001
4530.02.00.00.09.020.04.03.09.010.02.06.02.00.00.00.01.001
4543.04.02.02.011.015.05.06.011.010.04.05.02.02.00.00.01.001
4553.02.00.01.019.08.06.04.013.011.05.03.03.03.00.01.0-1.001